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    "info": {
        "author": "Sasha Illarionov",
        "author_email": "sasha.delly@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "* GEOpurify\n/Atlas of tools making Gene Expression Omnibus data amicable to machine learning./\n\n** Installation\n\n#+BEGIN_SRC sh\npip install GEOpurify\n#+END_SRC\n\n** Example Usage\n\n#+BEGIN_SRC python :results output org drawer\nfrom GEOpurify import GEOpurifier\ng = GEOpurifier()\ngds_df = g.gdspurify(\"GDS4376\")\n#+END_SRC\n\n** Methods\n\n*** ~filepurify(filepath, separation=\"\\t\")~\n\nGiven a path to a standard table with GEO data, returns a dataframe\nwith gene expression and GSM ids in separate columns.\n\n*** ~dirpurify(dirname)~\n\nGiven a path to a directory with standard tables of data from GEO,\napplies ~filepurify~ and return a combined dataframe.\n\n*** ~gdspurify(gds_id, load_extra_features=False)~\n\nGiven a GDS id, extracts data on a platform, platform organism,\nplatform techonolgy type and sample organism used. If\n~load_extra_features~ is set to ~True~, extra features are fetched\nfrom the GDS columns.\n\nSaves already processed tables corresponding to the GDS in the\ndirectory ~data/tmp~, while storing the raw GEO data in the directory\n~data/raw~.\n\n*** ~gdspolypurify(self, gds_list_path, load_extra_features=False)~\n\nGiven a path to a file listing GDS ids, each on a new line, applies\n~gdspurify~ to each and combines all the data into one dataframe.\n\n\n",
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        "keywords": "GEO,bioinformatics",
        "license": "MIT",
        "maintainer": "",
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        "name": "GEOpurify",
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        "requires_dist": [
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        "summary": "Making Gene Expression Omnibus data cleansing easy.",
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